Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R2), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site. 相似文献
Flyrock is an adverse effect produced by blasting in open-pit mines and tunneling projects. So, it seems that the precise estimations and risk level assessment of flyrock are essential in minimizing environmental effects induced by blasting. The first aim of this research is to model the risk level associated with flyrock through rock engineering systems (RES) methodology. In this regard, 62 blasting were investigated in Ulu Tiram quarry, Malaysia, and the most effective parameters of flyrock were measured. Using the most influential parameters on flyrock, the overall risk of flyrock was obtained as 32.95 which is considered as low to medium degree of vulnerability. Moreover, the second aim of this research is to estimate flyrock based on RES and multiple linear regression (MLR). To evaluate performance prediction of the models, some statistical criteria such as coefficient of determination (R2) were computed. Comparing the values predicted by the models demonstrated that the RES has more suitable performance than MLR for predicting the flyrock and it could be introduced as a powerful technique in this field. 相似文献
Ground vibration induced by rock blasting is one of the most crucial problems in surface mines and tunneling projects. Hence, accurate prediction of ground vibration is an important prerequisite in the minimization of its environmental impacts. This study proposes hybrid intelligent models to predict ground vibration using adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithms (GAs). To build prediction models using ANFIS, ANFIS–GA, and ANFIS–PSO, a database was established, consisting of 86 data samples gathered from two quarries in Iran. The input parameters of the proposed models were the burden, spacing, stemming, powder factor, maximum charge per delay (MCD), and distance from the blast points, while peak particle velocity (PPV) was considered as the output parameter. Based on the sensitivity analysis results, MCD was found as the most effective parameter of PPV. To check the applicability and efficiency of the proposed models, several traditional performance indices such as determination coefficient (R2) and root-mean-square error (RMSE) were computed. The obtained results showed that the proposed ANFIS–GA and ANFIS–PSO models were capable of statistically predicting ground vibration with excellent levels of accuracy. Compared to the ANFIS, the ANFIS–GA model showed an approximately 61% decrease in RMSE and 10% increase in R2. Also, the ANFIS–PSO model showed an approximately 53% decrease in RMSE and 9% increase in R2 compared to ANFIS. In other words, the ANFIS performance was optimized with the use of GA and PSO.
Natural Resources Research - Ground vibration generated from blasting is a detrimental side effect of the use of explosives to break the rock mass in mines. Therefore, accurately predicting ground... 相似文献
Ground vibration is one of the common environmental effects of blasting operation in mining industry, and it may cause damage to the nearby structures and the surrounding residents. So, precise estimation of blast-produced ground vibration is necessary to identify blast-safety area and also to minimize environmental effects. In this research, a hybrid of adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) was proposed to predict blast-produced ground vibration in Pengerang granite quarry, Malaysia. For this goal, 81 blasting were investigated, and the values of peak particle velocity, distance from the blast-face and maximum charge per delay were precisely measured. To demonstrate the performance of the hybrid PSO–ANFIS, ANFIS, and United States Bureau of Mines empirical models were also developed. Comparison of the predictive models was demonstrated that the PSO–ANFIS model [with root-mean-square error (RMSE) 0.48 and coefficient of determination (R2) of 0.984] performed better than the ANFIS with RMSE of?1.61 and R2 of 0.965. The mentioned results prove the superiority of the newly developed PSO–ANFIS model in estimating blast-produced ground vibrations. 相似文献
Strict control of the environmental impacts of blasting operations needs to be completely in line with the regulatory limits. In such operations, flyrock control is of high importance especially due to safety issues and the damages it may cause to infrastructures, properties as well as the people who live within and around the blasting site. Such control causes flyrock to be limited, hence significantly reducing the risk of damage. This paper serves two main objectives: risk assessment and prediction of flyrock. For these objectives, a fuzzy rock engineering system (FRES) framework was developed in this study. The proposed FRES was able to efficiently evaluate the parameters that affect flyrock, which facilitate decisions to be made under uncertainties. In this study, the risk level of flyrock was determined using 11 independent parameters, and the proposed FRES was capable of calculating the interactions among these parameters. According to the results, the overall risk of flyrock in the studied case (Ulu Tiram quarry, located in Malaysia) was medium to high. Hence, the use of controlled blasting method can be recommended in the site. In the next step, three optimization algorithms, namely genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were used to predict flyrock, and it was found that the GA-based model was more accurate than the ICA- and PSO-based models. Accordingly, it is concluded that FRES is a very useful for both risk assessment and prediction of flyrock.
Natural Resources Research - Prediction of ground vibration induced by blasting operations is a crucial challenge to engineers working in surface mines. This study aims to assess the efficiency of... 相似文献
Natural Resources Research - An ensemble technique namely gradient boosted tree (GBTs) and several optimized neural network models were hybridized to predict peak particle velocity (PPV) caused by... 相似文献
